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Recent research aims to automatically predict whether peer feedback is of high quality, e.g. suggests solutions to identified problems. While prior studies have focused on peer review of papers, similar issues arise when reviewing diagrams and other artifacts. In addition, previous studies have not carefully examined how the level of prediction granularity impacts both accuracy and educational utility. In this paper we develop models for predicting the quality of peer feedback regarding argument diagrams. We propose to perform prediction at the sentence level, even though the educational task is to label feedback at a multi-sentential comment level. We first introduce a corpus annotated at a sentence level granularity, then build comment prediction models using this corpus. Our results show that aggregating sentence prediction outputs to label comments not only outperforms approaches that directly train on comment annotations, but also provides useful information for enhancing peer review systems with new functionality.

Improving Peer Feedback Prediction: The Sentence Level is Right